X-ray image global enhancement algorithm in medical image classification

Wenzhong Zhu; Huanlong Jiang; Erli Wang; Yani Hou; Lidong Xian; Joyati Debnath

doi:10.3934/dcdss.2019089

Article Contents

2019, Volume 12, Issue 4&5: 1297-1309. Doi: 10.3934/dcdss.2019089 open access

This issue Previous Article Retraction: Xiaohong Zhu, Zili Yang and Tabharit Zoubir, Research on the matching algorithm for heterologous image after deformation in the same scene Next Article Efficient extraction algorithm for local fuzzy features of dynamic images

X-ray image global enhancement algorithm in medical image classification

1.
School of Computer Science, Sichuan University of Science & Engineering, Zigong, China
2.
School of Film and Television, Sichuan Vocational College of Cultural Industries, Chengdu, China
3.
Dept. of Mathematics and Statistics, Winona State University, Winona, MN 55987, USA

* Corresponding author: Wenzhong Zhu
* Corresponding author: Wenzhong Zhu

Received: June 2017

Revised: December 2017

Published: March 2019

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Abstract

The current global enhancement algorithm for medical X-ray image has problems of poor de-noising and enhancement effect and low reduction of the enhanced medical X-ray image. To address the problems, a global enhancement algorithm for X-ray image in medical image classification is proposed in this paper. The medical X-ray image is gray scaled, which provides the basis for the further processing of the image. The noise in medical X-ray image is removed by using multi-wavelet transform to improve the enhancement effect of the method. With the curve-let domain the medical X-ray image is enhanced, the reduction degree of medical X-ray image is improved and the global enhancement of the medical X-ray image is completed. Experimental results show that the de-noising effect of the proposed method is effective, the enhanced medical X ray image is better, and the reduction degree of medical X-ray image is high.

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Mathematics Subject Classification: 60B12.

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Figure 1. Gray contour line of image

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Figure 2. System structure of multi-wavelet decomposition and reconstruction

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Figure 3. Denoising results of three methods

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Figure 4. PSNR values of three methods

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Figure 5. Degree of reduction of three methods

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Table 1. Test results of three methods

Number of iterations	PSNR/dB			MSE/dp
Number of iterations	The proposed method	Retinex-based method	Double plateaus histogram-based method	The proposed method	Retinex-based method	Double plateaus histogram-based method
1	18.9672	13.2654	11.6587	824.839	965.325	978.547
2	18.9658	13.6548	12.3689	823.657	942.354	968.348
3	19.5781	12.6849	11.3589	836.348	951.347	946.256
4	19.6875	13.6528	10.3647	846.268	912.487	925.645
5	18.6597	11.3549	12.0367	851.267	937.985	971.648
6	20.3698	12.4872	9.2657	865.215	978.654	985.157
7	21.8571	11.8627	9.5489	836.259	996.125	977.627
8	24.6257	10.6894	12.3647	841.025	984.367	955.348
9	23.1459	10.8547	10.3658	823.024	971.254	957.518
10	22.6587	9.3657	9.6581	856.237	956.185	975.264

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